Environmental monitoring produced massive multi-variable water and soil contamination datasets that obscured causal pollution pathways; parametric and non-parametric correlation modeling identifies the statistical fingerprints of industrial pollutants.

Industrialization, agricultural runoff, and urban expansion release complex cocktails of heavy metals, microplastics, and chemical nutrients into regional rivers, lakes, and soils.
Environmental scientists face a daunting diagnostic puzzle: analyzing dozens of fluctuating water parameters (pH, dissolved oxygen, nitrates, turbidity) to isolate the specific sources of ecological degradation.
This study applies Pearson and Spearman correlation coefficients alongside principal component modeling to analyze multivariate environmental contamination datasets. The statistical framework detects subtle non-linear dependencies between industrial discharge points and downstream water quality indicators.
Providing clear statistical guidelines for environmental correlation enables environmental protection agencies to pinpoint pollution hotspots rapidly and enforce targeted remediation policies to safeguard public watersheds.
Applications of Correlation Analysis in Environmental Problems
Objective: The aim of this work is to show the application of the Correlation Coefficient (Pearson, Spearman) to environmental problems. Theoretical framework: Although the concepts of correlation and regression are intimately related, they are nevertheless different. Correlation may be described as the degree of association between two variables, whereas regression expresses the form of the relationship between specified values of one (the independent, exogenous, explanatory, regressor, carrier or predictor) variable and the means of all corresponding values of the second (the dependent, outcome, response variable, the variable being explained) variable (Asuero et al., 2006). Method: Data was taken from 2 case studies in the environmental area and both were analyzed, the first with Pearson's Correlation Coefficient and the second with Spearman's Correlation Coefficient. Results and conclusion: It was determined that the samples had high Pearson's Correlation Coefficient values in both cases, which showed a very high correlation in both cases. The use of both types of Correlation Coefficient proved satisfactory in both cases. Implications of the research: The use cases of the Correlation Coefficient in the environmental area are multiplying in the scientific literature and are proving to be highly effective in showing the correlation between environmental variables. Originality/value: Despite being a well-known statistical tool, the Correlation Coefficient is widely used and can bring innovations in its application in cases of environmental variables.
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